Последние достижения в методах искусственного интеллекта для диагностики рака молочной железы

PDF файл (Скачать)

МРНТИ 34.57.23, 34.17.53 №1 (2021г.) Машекова А., Жао Я., Нг Э.Й.К., Зарикас В., Мұхметов О. Одной из наиболее частых причин смерти молодых женщин от болезней почти во всех странах мира является рак груди. Правильная и своевременная диагностика рака молочной железы жизненно важна, так как его раннее выявление значительно помогает при дальнейшем лечении. Есть несколько методов выявления рака груди. В этой статье рассматривается, такие методы диагностирования как маммография и термография. Для обоих методов существует множество исследовательских подходов, в которых используются компьютерные системы обнаружения для улучшения выявляемости рака груди. Большинство разработок основаны на последних достижениях в области методов машинного обучения, численного моделирования и статистических методов. Они охватывают широкий спектр области искусственного интеллекта (ИИ). В статье подробно рассматриваются возможные пути прогресса в области искусственного интеллекта для диагностики рака груди. Ключевые слова: рак молочной железы, термография, искусственный интеллект, сверточная нейронная сеть, байесовские сети, машинное обучение. Список литературы 1 Francis, S. V., Sasikala, M., Jaipurkar, S. D. Detection of Breast Abnormality Using Rotational Thermography. In Application of Infrared to Biomedical Sciences – Singapore: Springer, 2017 – С 133 – 158. 2 Sheeja F. V., Sasikala M., Bharathi G. B., Jaipurkar S. D. Breast cancer detection in rotational thermography images using texture features // Infrared Physics & Technology – 2014 – № 67 – С 490 – 496. 3 Breast Cancer Treatment (PDQ®)”. NCI. 23 May 2014. Archived from the original on 5 July 2014. Retrieved 29 June 2014 4 World Cancer Report 2014. World Health Organization. 2014. pp. Chapter 5.2. ISBN 978-92-832-0429-9 5 HealthGrove. 2013. Breast Cancer in Kazakhstan. Retrieved November 15, 2017, from http://global-disease-burden.healthgrove.com/l/33097/ Breast-Cancer-in-Kazakhstan 6 Sejtkazina, G. D., Bajpeisov, D. M., Sejsenbaeva, G. T., Oncological services RK. A. A. P. Kazakhstan for 2011(statistical material) / Indicators Oncology Service of the Republic of Kazakhstan for 2013 (statistical material), 2012. 7 Apsalikov B.A., Manambaieva Z.A., Belikhina T.I., Adilkhanov T.A., Dauletiarova M.A., Apsalikov K.N. The dynamics of morbidity of breast cancer in the Eastern-Kazakhstanskaia oblast and role of radiation factor // Problem isocialnoi gigieni, zdravookhranenia i istorii meditsini (Problems of social hygiene, public health and history of medicine, Russian journal) – 2016 – № 24 (1) – С 7 – 10. DOI: 10.1016/0869-866X-2016-1-7-10 8 Minsky M. Has AI contributed to an understanding of the human mind? Mind Design J. Haugeland, Ed. / Cambridge, MA: MIT Press, 1981 – С 95 – 128. 9 Oliver A. A review of automatic mass detection and segmentation in mammographic images // Med I A – 2010 – № 14 (2) – С 87 – 110. 10 Domingues I., Cardoso J. S. Mass detection on mammogram images: a first assessment of deep learning techniques / Cambridge, MA, 2013 – 200 с 11 Dhungel N., Carneiro G., Bradley A. P.. Automated mass detection in mammograms using cascaded deep learning and random forests. // Internation al Conference on Digital Image Computing: Techniques and Applications. IEEE, 2015 – С. 1–8 12 Ertosun M. G., Rubin D. L. Probabilistic visual search for masses within mammography images using deep learning. // IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2015 - С 1310–1315 13 Huynh B. Q., Li H., Giger M. L.. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks // Journal of Medical Imaging – 2016 № 3 (3) – С 501-509 14 Levy D., Jain A. Breast mass classification from mammograms using deep convolutional neural networks. Journal of Medical Imaging – 2017 № 4 (2) – С 105-111 15 Arevalo J., Gonzalez F. A., Ramos-Poll R., Oliveira J. L., Lopez M.A.G. Representation learning for mammography mass lesion classification with convolutional neural networks // Computer methods and programs in biomedicine – 2016 – № 127 – С 248–257. 16 Mordang J. J., Janssen T., Bria A., Kooi T., Gubern-Merida A., Karssemeijer N. Automatic microcalcification detection in multivendor mammography using convolutional neural networks. // Springer: International Workshop on Digital Mammography, 2016 – С. 35–42 17 Carneiro G., Nascimento J., Bradley A. P. Unregistered multiview mammogram analysis with pre-trained deep learning models // International Conference on Medical Image Computing and Computer Assisted Intervention. Springer, 2015, – С. 652–660 18 Becker A. S., Marcon M., Ghafoor S., Wurnig M. C., Frauenfelder T., Boss A. Deep learning in mammography: Diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer / USA: Investigative Radiology, 2017 19 Warren R. Screening Women at High Risk of Breast Cancer on the Basis of Evidence. // Eur. J. Radiol. – 2001 – № 39 (1) – С 50–59 20 E.H. Shortliffe. Computer-Based Metrical Consultations: MYCIN / North Holland, New York: Elsevier, 1976. 21 Miller R.A., Masarie F. E. J. Use of the Quick Medical Reference (QMR) program as a tool for medical education // Methods of Information in Medicine – 1989 - № 28 – С 340 - 346 22 Yang Y.; Hu J.; Liu Y.; Chen X. A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three-way decision theory. // Expert Systems – 2019 - № 36 (3) – С 15-25 23 Gao Q.; Dong A. A Contextual Reasoning Research Based on the Uncertain Context. // Proceedings of the 3rd IEEE International Conference on Robotic Computing; IEEE: Naples, Italy, 2019 24 Jha S.K.; Pan Z.; Elahi E.; Patel N. A comprehensive search for expert classification methods in disease diagnosis and prediction. Expert Systems 2019, 36 (1) 25 Tselykh A., Tselykh L., Vasilev V., Barkovskii S. Knowledge discovery using maximization of the spread of influence in an expert system. // Expert Systems – 2018 - № 35 (6) 26 Nakasima-López S., Sanchez M.A., Castro, J.R. Big Data and Computational Intelligence: Background, Trends, Challenges, and Opportunities. // Studies in Systems, Decision and Control – 2018 № 143 – С. 183-196 27 Ung, S.-T. Development of a weighted probabilistic risk assessment method for offshore engineering systems using fuzzy rule-based Bayesian reasoning approach. // Ocean Engineering – 2018, № 147 – С 268-276 28 Quinn S., Bond R., Nugent C. Ontological modelling and rule-based reasoning for the provision of personalized patient education. // Expert Systems – 2017 - № 34 (2) 29 Mukhmetov O., Igali D., Zhao Y., Fok S. Ch., Teh S. L., Mashekova A., Ng EYK. Finite Element Modelling for the Detection of Breast Tumor. // 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering, Taiwan. DOI 10.1109/BIBE.2018.00078 30 Igali D., Mukhmetov O., Zhao Y., Fok S. Ch., Teh S. L. An Experimental Framework for Validation of Thermal Modeling for Breast Cancer Detection. // IOP Conf. Series: Materials Science and Engineering, 2018 – 408c doi:10.1088/1757-899X/408/1/012031 31 Mukhmetov O., Mashekova A., Zhao Y., Kwee N.Y. Inverse thermal modeling and experimental validation for breast tumor detection by using highly personalized surface thermal patterns and geometry of breast (manuscript id: JMES-20-0347). // Part C: Journal of Mechanical Engineering Science, 2020 (accepted) 32 Zhao Y., Myrzhakhmet A., Mashekova A., Ng EYK, Mukhmetov O. 3D numerical study of temperature patterns in a female breast with tumor using a realistic multi-layered model. // Bulletin of National Academy of Science of the Republic of Kazakhstan, 2021 (accepted) 33 Keyserlingk J. R., Ahlgren P. D., Yu E., Belliveau N., Yassa M. Functional Infrared Imaging of the Breast. // IEEE Eng. Med. Biol. Mag. – 2000 - № 19 – С 30-41. 34 Head J.F., Elliott R.L. Infrared Imaging: Making Progress in Fulfilling its Medical Promise. // IEEE Eng. Med. Biol. Mag. – 2002 - № 21 – С 80-85. 35 Ng E.Y.K. A Review of Thermography as Promising Noninvasive Detection Modality for Breast Tumor // International Journal of Thermal Sciences – 2008 - № 48 - С 849-859 36 Ng E.Y.K., Kee E.C.Advanced integrated technique in breast cancer thermography. // J. Med. Eng. Technol. – 2008 - № 32(2) – С 103-114 37 Mital M., Pidaparti R.M. Breast tumor simulation and parameters estimation using evolutionary algorithms. // Model Simul Eng, - 2008 - № Special Issue - С 6 - 12 38 Kandlikar S.G., Perez-Raya I., Raghupathi P.A., Gonzalez-Hernandez J.S., Dabydeen D., Medeiros L., Phatak P. Infrared Imaging technology for breast cancer detection - Current status, protocols and new directions. // International Journal of heat and mass transfer – 2017 - № 108 – С 2303-2320 39 Saniei E., Setayeshi S., Akbari M.E., Navid M. Parameter estimation of breast tumour using dynamic neural network from thermal pattern. // Journal of Adanced Research – 2016 - № 7 – С 1045-1055 40 Wahab A.A., Mohamad Salim M.I., Yunus J., Che Aziz M.N. Tumor localization in breast thermography with various tissue compositions by using Artificial Neural Network. // 2015 IEEE Student conference on Research and Development, 2015 – С 484-488 41 Pramanik S., Bhattacharje D., Nasipuri M. Texture analysis of breast thermogram for differentiation of malignant and benign breast // International Conference on Advances in Computing, Communications and Informatics, ICACCI, 2016 – С 8-14 42 Raghavendra U., Rajendra Acharya U., Ng E. Y-K, Tan Jen-Hong, Gudigar A. An Integrated Index for Breast Cancer Identification using Histogram of Oriented Gradient and Kernel Locality Preserving Projection Features Extracted from Thermograms // Quantitative Infrared Thermography Journal – 2016 - № 13 (2) – С 195-209 43 Etehadtavakol M., Emrani Z., Ng E. Y. K. Rapid Extracting of the Hottest or Coldest Regions of Medical Thermographic Images // Medical & Biological Engineering & Computing, 2019 - № 57(2) – С 379–388, https://doi.org/10.1007/ s11517-018-1876-2 44 Raghavendra U., Gudigara A, Rao T. N., Ciaccio E. J., Ng E.Y.K., Acharya U. R, Computer aided diagnosis for the identification of breast cancer using thermogram images: A comprehensive review // Infrared Physics & Technology – 2019 - № 102 – С 103 - 123. https://doi.org/10.1016/j.infrared.2019.103041 45 Jensen F.V. An Introduction to Bayesian Networks / New York: Springer, 1996 46 Bapin Y., Zarikas V. Smart building’s elevator with intelligent control algorithm based on Bayesian networks. // International Journal of Advanced Computer Science and Applications – 2019 № 10(2) – С 16-24 47 Amrin A., Zarikas V., Spitas C. Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra” framework. // Reliability Engineering and System Safety – 2018 - № 180 – С 211-225 48 Zarikas V., Papageorgiou E., Pernebayeva D., Tursynbek N. Medical decision support tool from a fuzzy-rules driven Bayesian network. // Proceedings of the 10th International Conference on Agents and Artificial Intelligence; SciTePress: Funchal, Portugal, 2018 49 Zarikas V., Papageorgiou E., Regner P. Bayesian network construction using a fuzzy rule based approach for medical decision support. // Expert Systems – 2015. №32(3). - С.344-369.

Создатель : Лариса Николаевна Гребцова

Создан : 22 августа 2024